article

Object tracking: A survey

Authors:

Alper Yilmaz,

Omar Javed, and

Mubarak ShahAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 38, Issue 4

Pages 13 - es

https://doi.org/10.1145/1177352.1177355

Published: 25 December 2006 Publication History

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Abstract

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

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de Rochechouart MAbu Zitar REl Fallah Seghrouchni ABarbaresco FDastani MSichman JAlechina NDignum V(2024)End to End Camera only Drone Detection and Tracking Demo within a Multi-agent Framework with a CNN-LSTM Model for Range EstimationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663291(2797-2799)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663291
Karpuzov SPetkov GIlieva SPetkov AKalitzin S(2024)Object Tracking Based on Optical Flow Reconstruction of Motion-Group ParametersInformation10.3390/info1506029615:6(296)Online publication date: 22-May-2024
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Index Terms

Object tracking: A survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding

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Reviews

Reviewer: Sebastien Lefevre

Object tracking is one of the major steps toward understanding video content. Indeed, its goal is to give object positions in the successive frames of a video sequence. This spatio-temporal information can then be used to analyze the actions or behavior of the related objects. Object tracking is a mandatory step in many video-based applications, such as surveillance, traffic monitoring, sport event analysis, active vision and robotics, and medical image sequence analysis. Thus, there has been a lot of research in this field over the last 20 years, and it is quite difficult to determine the method to be used when a particular video application is considered. The survey proposed by Yilmaz, Javed, and Shah intends to point out the key aspects and to describe the major (context-free) approaches for object tracking in color video sequences. An entire book could be devoted to this subject. In this 46-page paper, the authors have decided to present most of the main elements in object tracking rather than trying to give an exhaustive view of some object tracking-related problems. The paper is aimed at the image processing engineer or scientist. This comprehensive and well-illustrated survey contains several parts, each dedicated to one of the main elements involved in object tracking. The paper answers the following questions. How is the object to be tracked modeled__?__ How are the object model and the image data associated__?__ How is the object extracted from the sequence__?__ How is the tracking process performed__?__ The authors describe some applications where object tracking is necessary and explain why this can be a particularly difficult task. They then describe the different shape and appearance models that can be associated to an object and present the image features to be used in object tracking. Next, they tackle the problem of object detection, which should often be solved before dealing with tracking itself. The core part of this paper, Section 5 on object tracking, contains a presentation of the main existing approaches gathered in three classes: point, kernel, and silhouette tracking. The paper ends with tackling some related issues, such as occlusion (when an object is temporarily hidden by another one) and multiple camera tracking (particularly useful for video surveillance in large and complex environments). Yilmaz, Javed, and Shah assert, finally, that a generic tracking system can be reached only if it involves contextual information in some way. The authors'; attempt to give an overview of object tracking is nearly successful. Trying to deal with all aspects of object tracking in a single paper (even of 46 pages) leads to some omissions. In particular, the reader will not find in this survey descriptions of the different motion models, computational complexities, and parameter settings. Moreover, the case of very small objects (a few pixels) is not considered. To cover all of this material, though, the authors would have had to consider only one aspect of tracking, and the reader would not receive a global presentation of the object tracking problem.

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ACM Computing Surveys Volume 38, Issue 4

2006

153 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1177352

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Published: 25 December 2006

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